I used the Classification trees, RF (random forest), kNN (k-nearest neighbor) and NB (naïve Bayes) methods in Orange Canvas for multi-class classification problem, do they all support multi-class classification? which stratagy do they use (detailed stratagy of each individual classifier)?

Ales wrote:All of the mentioned methods support multi-class classification intrinsically. Please refer to the documentation and relevant references.

I also believe they support multi-class classification intrinsically, but I have a question about kNN multi-class classification. In binary classification, an object is classified by a majority vote of its neighbors, in multi-class classification, sometimes, the vote could be equal. For example, an object, the 5 nearest neighbors, 1 of the neighbors belongs to class"1", 2 belongs to class "2", and 2 belongs to class "3", then which class will the object classified?

A random choice between class "2" and "3" (but a consistent random choice i.e. the instance for which the classification is sought is used to seed the random generator, so classification on the same instance always returns the same class).

Ales wrote:A random choice between class "2" and "3" (but a consistent random choice i.e. the instance for which the classification is sought is used to seed the random generator, so classification on the same instance always returns the same class).

Will the other classifier(i.e. RF, NB and CT) have the same problem (cannot give a exact choice but a random choice)?